Building a Computerized Disease Registry for Chronic Illness Management of Diabetes
Jeffrey Hummel, MD, MPH
The University of Washington Physicians Network (UWPN) is a nonprofit primary care delivery system in Seattle with nine clinics linked by a computerized, networked clinical information system. In 1998, UWPN initiated a diabetes management program in collaboration with the Institute for Healthcare Improvement. In the process of developing the diabetes program, several successive types of registry systems were constructed using a computerized spreadsheet and a relational database. This project has clarified some of the challenges that any delivery system seeking to construct a diabetes management program must face regardless of whether they have paper charts or a completely computerized medical record system. As more providers make the transition to computerized clinical information systems, there will be greater opportunities to use the information residing in electronic charts for population-based care of chronic diseases like diabetes.
This article reviews the conceptual framework for chronic illness management programs. It then addresses specific key issues pertaining to construction of a registry for diabetic patients using readily available software.
The Conceptual Framework for
Chronic Illness Management
Research has identified four essential tasks that people with chronic illnesses must perform if they are to keep their risk of excess morbidity to a minimum.1 They must
Likewise, there are three essential activities that health care systems must carry out for patients with chronic illness. They must
Although the above tasks seem relatively simple, some of them are often overlooked in the course of scheduled office visits. Patients and delivery systems often fall short of achieving optimal clinical outcomes.2,3
Following are reasons why this is the case, despite the fact that physicians and other members of the health care system strive to provide the best care possible.
The evidence for the effectiveness of chronic illness programs is based in successful clinical trials. However, such clinical trials have several organizational characteristics that set them apart from the usual office practice environment in which busy clinicians attempt to achieve outcomes similar to those described in the trials.4
Wagner and associates have developed a model for chronic illness management in clinical practice based on the system characteristics shown to improve outcomes in a number of chronic illnesses (Fig. 1).5 The model postulates the crucial dynamic in care delivery to be an informed and activated patient interacting with a prepared and proactive practice team to support self-management and behavior change. The basic categories of available resources and tools are 1) community services and policies, 2) decision support, 3) delivery system design, and 4) clinical information systems.
Community services and policies include public and private agencies outside the health care delivery system offering resources for patients and their families.
Decision support includes evidence-based guidelines and point-of-care reminders to minimize variation in the clinical strategies used throughout a delivery system in the management of patients with a chronic illness.6
Delivery system design refers to changes in processes of care delivery that help providers carry out the tasks of chronic illness care amid the competing demands of a busy office practice.
There are five types of delivery system design changes that have been shown to improve chronic illness outcomes. They are categorized as follows:
Clinical information systems refers to the organization of clinical data from patient medical records to coordinate the delivery of interventions and self-management support activities of the entire population. Creation of a disease registry is crucial in the management of a population of patients with chronic illness.11
Further research is needed to determine the relative importance of each of the four components of the chronic illness model. However, to achieve meaningful improvement in outcomes, a program must include some type of registry to identify and track patients, at least one element of delivery system design change, and some type of decision support.12,13
The Computerized Disease
Registry for Diabetes
How Do You
Identify Patients to Include?
All of these methods will result in the inclusion of some patients who do not have diabetes and occasionally miss some patients who do. The most practical approach is to use the above methods to identify candidates for inclusion in the diabetes registry. Then, as a second step, someone with clinical expertise can place on the registry those patients who clearly have diabetes.
A registry must include a mechanism to distinguish between patients who have left a practice and those who have simply not come in for a visit. Most patients with diabetes will be seen at least 3 times in 12 months at a primary care office. Any patient who has not been in contact with the clinic in 12 months should be contacted to determine whether they are overdue for monitoring or whether they have left the practice.
This is important for two reasons: 1) provider acceptance of outcome data is directly related to its perceived reliability, and 2) if a change in the care process is implemented as a pilot project, it is essential to be able to determine whether that innovation results in an improvement. If the innovation increases the chance of detecting patients who have left the practice, the noninnovation group will appear to have worse outcomes because the denominator will include more patients who have actually left the practice but appear to be overdue for an intervention.
The registry should include all patients with diabetes for whom the program using the registry is responsible. Responsibility for diabetes crosses several specialty interfaces, the most important of which is the interface between primary care and endocrinology. There may be no reason to include in a primary care registry patients for whom all aspects of diabetes care are managed by endocrinology. In some cases, glycemic control may be followed by an endocrinologist whereas risk factors for cardiovascular disease, such as hypertension, lipids, and smoking cessation, are issues for which primary care is responsible. In this situation, data from specialty visits should be included in the primary care registry to avoid redundant testing and confusion over whether the primary care physician or the endocrinologist is responsible for a particular aspect of the patient's illness. Shared management arrangements require close collaboration between primary care and endocrinology.
What Information Goes in the
Additional data fields, as outlined below, should be considered for each of the categories depending on the ease of data capture and utility of the data.
Where Do You Get the Data?
An EMR in normal operation is perpetually in motion and constantly changing. During the day, clinical data are added continuously, wheras at night, data are entered by results reporting and home access by providers on call.
Before clinical data from patient visits, laboratory results, and pharmacy can be used for a registry or any kind of reporting, they must be downloaded into a static form either as a spreadsheet or a relational database, as shown in Figure 3. For this to occur, a relational database must be built with defined data fields to contain each specific data type in the registry. One such data field, for example, would be date of last urine albumin/creatinine ratio, whereas on another would be the value for that test. Programmed instructions for downloading data are then written so that each selected data type in the electronic medical record will be routed to the appropriate data field.
Data downloads can be scheduled to occur nightly so that the data for any given report are never more than 24 h old. The interface for this kind of download can be a challenge to maintain as software programs are upgraded, laboratories make changes in their tests, and medications are added to or deleted from a formulary.
In delivery systems with less than full computerization, there are still ways to use electronic records in a registry. In small or individual medical practices, demographics such as age, sex, payor, and zip code are often obtainable from computerized billing records. Likewise, it may be possible to obtain laboratory data on specific patients that can be entered electronically into a registry.
On the other hand, even with a full EMR, clinical staff may enter data into the chart as text but not in a form that is accessible to a computer query. To be accessible, information relevant to disease management must reside in a data field. For example, if the results of monofilament screening for neuropathy are buried in a chart note, the only way they can be recovered is by chart review. A data entry window for results of neuropathy screening will enter the data into a specific field in the EMR, which can be downloaded to the registry and accessed by query.
A similar issue is encountered with blood pressure, which if entered in standard format in a data field as a string variable cannot be used to reliably identify patients with hypertension. The blood pressure must first be converted to two separate data fieldsone for systolic and one for diastolic pressureso that it can be formatted as a numeric variable. This allows a query to be written identifying patients with either systolic or diastolic hypertension.
Whose Job Is It?
How Will the Registry Be Used?
In general, providers need the names of patients who need specific things done within a specific time frame. For example, a delivery system may monitor quality of care based on the percentage of diabetic patients who have had an HbA1c measured within 6 months. What providers then need is a list each month of the patients with diabetes who have not had an HbA1c measured within 5 months. A team member can then call those patients with a reminder to be tested. In this way, the team has time to act on the information from the registry to improve the outcomes and meet the organizational goals.
Likewise, the organization may select a target of less than some percentage of their patients with diabetes having an HbA1c >7, 8, or 9%. The providers need an accurate and timely list of patients whose most recent HbA1c is greater than the target so they can be brought in for more intense intervention. The report can provide similar information for coronary risk factor reduction, such as lipids and blood pressure testing, or for end organ disease monitoring, including microalbumin screening. The report should be directly linked to explicit organizational goals for improved outcomes with clear tasks for the team to perform to achieve the goals.
The frequency of the reports will depend on their use and on how often the data in the registry are updated. For a diabetes management program run by a chronic illness nurse, weekly reports may be crucial, particularly if the reports can be automatically generated from a database that is updated nightly. In a fully automated EMR with a relational database, it may be possible to run the reports daily and post them on an Intranet URL, which anyone on the clinical team can access. On the other hand, if data are hand entered into a spreadsheet and reports take a significant amount of time to prepare, monthly or even quarterly reporting may suffice.
How Should the Registry Be
For beginning programs, it makes sense to start with a spreadsheet. Pilot projects, demonstration projects, and other small-scale efforts to develop disease management experience will often be best served by using a spreadsheet that is maintained by a person intimately involved in the use of the data the spreadsheet contains.
The tasks associated with developing a relational database, if begun at the start of a chronic illness program, are complex enough that efforts to make the information system work properly may impede other aspects of the program. If a small-scale project is successful enough to continue and spread to the entire organization, it will probably be necessary to convert to a relational database. A disease management program that is mature enough to function out of several clinics and has sufficient organizational support to have allocated to it dedicated informatics resources will find a registry using a relational database much more efficient.
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Jeffrey Hummel, MD, MPH, is Director of Research and Clinical Improvement at the University of Washington Physicians Network in Seattle.
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